Appearance Shock Grammar for Fast Medial Axis Extraction from Real Images

• URL: http://arxiv.org/abs/2004.02677v1
• Date: Mon, 6 Apr 2020 13:57:27 GMT
• Title: Appearance Shock Grammar for Fast Medial Axis Extraction from Real Images
• Authors: Charles-Olivier Dufresne Camaro, Morteza Rezanejad, Stavros Tsogkas, Kaleem Siddiqi, Sven Dickinson
• Abstract summary: We combine ideas from shock graph theory with more recent appearance-based methods for medial axis extraction from complex natural scenes. Our experiments on the BMAX500 and SK-LARGE datasets demonstrate the effectiveness of our approach.
• Score: 10.943417197085882
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We combine ideas from shock graph theory with more recent appearance-based methods for medial axis extraction from complex natural scenes, improving upon the present best unsupervised method, in terms of efficiency and performance. We make the following specific contributions: i) we extend the shock graph representation to the domain of real images, by generalizing the shock type definitions using local, appearance-based criteria; ii) we then use the rules of a Shock Grammar to guide our search for medial points, drastically reducing run time when compared to other methods, which exhaustively consider all points in the input image;iii) we remove the need for typical post-processing steps including thinning, non-maximum suppression, and grouping, by adhering to the Shock Grammar rules while deriving the medial axis solution; iv) finally, we raise some fundamental concerns with the evaluation scheme used in previous work and propose a more appropriate alternative for assessing the performance of medial axis extraction from scenes. Our experiments on the BMAX500 and SK-LARGE datasets demonstrate the effectiveness of our approach. We outperform the present state-of-the-art, excelling particularly in the high-precision regime, while running an order of magnitude faster and requiring no post-processing.

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